U.S. patent number 10,592,269 [Application Number 15/658,038] was granted by the patent office on 2020-03-17 for dynamic code deployment and versioning.
This patent grant is currently assigned to Amazon Technologies, Inc.. The grantee listed for this patent is Amazon Technologies, Inc.. Invention is credited to Derek Steven Manwaring, Sean Philip Reque, Dylan Chandler Thomas, Timothy Allen Wagner, Xin Zhao.
United States Patent |
10,592,269 |
Wagner , et al. |
March 17, 2020 |
Dynamic code deployment and versioning
Abstract
A system for providing dynamic code deployment and versioning is
provided. The system may be configured to receive a first request
to execute a newer program code on a virtual compute system,
determine, based on the first request, that the newer program code
is a newer version of an older program code loaded onto an existing
container on a virtual machine instance on the virtual compute
system, initiate a download of the newer program code onto a second
container on the same virtual machine instance, and causing the
first request to be processed with the older program code in the
existing container.
Inventors: |
Wagner; Timothy Allen (Seattle,
WA), Reque; Sean Philip (Everett, WA), Manwaring; Derek
Steven (Lynnwood, WA), Zhao; Xin (Seattle, WA),
Thomas; Dylan Chandler (Seattle, WA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Amazon Technologies, Inc. |
Seattle |
WA |
US |
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Assignee: |
Amazon Technologies, Inc.
(Seattle, WA)
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Family
ID: |
55584497 |
Appl.
No.: |
15/658,038 |
Filed: |
July 24, 2017 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20180039506 A1 |
Feb 8, 2018 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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14502620 |
Sep 30, 2014 |
9715402 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F
9/44536 (20130101); G06F 9/44552 (20130101); G06F
9/45558 (20130101); G06F 2009/45575 (20130101); G06F
2009/4557 (20130101) |
Current International
Class: |
G06F
9/445 (20180101); G06F 9/455 (20180101) |
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Primary Examiner: Brophy; Matthew J
Attorney, Agent or Firm: Knobbe Martens Olson & Bear
LLP
Parent Case Text
This application is a continuation of U.S. application Ser. No.
14/502,620, filed Sep. 30, 2014 and titled "DYNAMIC CODE DEPLOYMENT
AND VERSIONING," the disclosure of which is hereby incorporated by
reference in its entirety.
The present application's Applicant previously filed the following
U.S. patent applications Ser. No. on Sep. 30, 2014, the disclosures
of which are hereby incorporated by reference in their
entireties:
TABLE-US-00001 U.S. Application No. Title 14/502,589 MESSAGE-BASED
COMPUTATION REQUEST SCHEDULING 14/502,810 LOW LATENCY COMPUTATIONAL
CAPACITY PROVISIONING 14/502,714 AUTOMATIC MANAGEMENT OF LOW
LATENCY COMPUTATIONAL CAPACITY 14/502,992 THREADING AS A SERVICE
14/502,648 PROGRAMMATIC EVENT DETECTION AND MESSAGE GENERATION FOR
REQUESTS TO EXECUTE PROGRAM CODE 14/502,741 PROCESSING EVENT
MESSAGES FOR USER REQUESTS TO EXECUTE PROGRAM CODE
Claims
What is claimed is:
1. A system, comprising: one or more processors; and one or more
memories, the one or more memories having stored thereon
instructions, which, when executed by the one or more processors,
cause the one or more processors to: receive a first request to
execute a first program code, the first request including (i) an
identifier that uniquely identifies the first program code and (ii)
one or more execution parameters for executing the first program
code; initiate, using the identifier included in the first request
to execute the first program code, a download of the first program
code onto at least one of a virtual machine instance or a data
store associated with the virtual machine instance; determine that
a second program code different from the first program code is
loaded on the virtual machine instance or the data store; and prior
to completion of the download of the first program code, cause the
second program code to be executed using the one or more execution
parameters included in the first request to execute the first
program code despite the identifier identifying the first program
code and not the second program code.
2. The system of claim 1, wherein the instructions, when executed
by the one or more processors, further cause the one or more
processors to: receive a second request to execute the first
program code, the second request including the identifier
identifying the first program code and one or more execution
parameters for executing the first program code; and cause the
first program code to be executed using the one or more execution
parameters included in the second request without downloading
another copy of the first program code.
3. The system of claim 1, wherein the instructions, when executed
by the one or more processors, further cause the one or more
processors to: receive a second request to execute the first
program code, the second request including the identifier
identifying the first program code; and determine that the download
of the first program code has not been completed; and cause the
second program code to be executed using the one or more execution
parameters included in the second request.
4. The system of claim 1, wherein the first program code is a newer
version of the second program code.
5. The system of claim 1, wherein the instructions, when executed
by the one or more processors, further cause the one or more
processors to initiate a download of the second program code onto
the at least one of the virtual machine instance or the data store
in response to receiving a second request to execute the second
program code, wherein the second request is received prior to the
first request.
6. The system of claim 1, wherein the second program code is stored
in a container created on the virtual machine instance, the
instructions, when executed by the one or more processors, further
causing the one or more processors to cause the second program code
to be executed inside the container.
7. The system of claim 6, wherein the instructions, when executed
by the one or more processors, further cause the one or more
processors to: receive a second request to execute the first
program code, the second request including the identifier
identifying the first program code; and subsequent to completion of
the download of the first program code, cause the first program
code to be copied onto the container; and cause the first program
code to be executed inside the container.
8. The system of claim 6, wherein the instructions, when executed
by the one or more processors, further cause the one or more
processors to: receive a second request to execute the first
program code, the second request including the identifier
identifying the first program code; and subsequent to completion of
the download of the first program code, cause the first program
code to be copied onto a second container created on the virtual
machine instance; and cause the first program code to be executed
inside the second container.
9. A computer-implemented method comprising: receiving a first
request to execute a first program code, the first request
including (i) an identifier that uniquely identifies the first
program code and (ii) one or more execution parameters for
executing the first program code; initiating, using the identifier
included in the first request to execute the first program code, a
download of the first program code onto at least one of a virtual
machine instance or a data store associated with the virtual
machine instance; determining that a second program code different
from the first program code is loaded on the virtual machine
instance or the data store; and prior to completion of the download
of the first program code, executing the second program code using
the one or more execution parameters included in the first request
to execute the first program code despite the identifier
identifying the first program code and not the second program
code.
10. The computer-implemented method of claim 9, further comprising:
downloading the first program code onto an internal data store
accessible by the virtual machine instance and at least one other
virtual machine instance; subsequent to completion of the download,
copying the first program code from the internal data store onto a
container created on the virtual machine instance; and executing
the first program code inside the container.
11. The computer-implemented method of claim 9, further comprising:
downloading the first program code onto a code cache of the virtual
machine instance, wherein the code cache is not accessible by any
virtual machine instance other than the virtual machine instance;
subsequent to completion of the download, copying the first program
code onto a container created on the virtual machine instance; and
executing the first program code inside the container.
12. The computer-implemented method of claim 11, further
comprising: determining that the second program code is no longer
needed; and removing the second program code from the code cache of
the virtual machine instance.
13. The computer-implemented method of claim 9, further comprising:
downloading the first program code onto a container created on the
virtual machine instance; and subsequent to completion of the
download, executing the first program code inside the
container.
14. The computer-implemented method of claim 9, further comprising:
determining that a first container of the virtual machine instance
having the first program code loaded thereon is busy; determining
that a second container of the virtual machine instance having the
second program code loaded thereon is not busy; and executing the
second program code inside the container.
15. The computer-implemented method of claim 9, further comprising:
determining that the first program code is a newer version of the
second program code stored on the virtual machine instance and that
the virtual machine instance does not have the first program code
loaded thereon; and executing the second program code on the
virtual machine instance.
16. The computer-implemented method of claim 9, further comprising:
determining that the first program code is a newer version of the
second program code stored on the virtual machine instance and that
the virtual machine instance has a container in which the second
program code is being executed; and subsequent to completion of the
execution of the second program code in the container, loading the
first program code onto the container.
17. Non-transitory physical computer storage storing instructions,
which, when executed by one or more processors, cause the one or
more processors to: receive a first request to execute a first
program code, the first request including (i) a identifier that
uniquely identifies the first program code and one or more
execution parameters for executing the first program code;
initiate, using the identifier included in the first request to
execute the first program code, a download of the first program
code onto at least one of a virtual machine instance or a data
store associated with the virtual machine instance; determine that
a second program code different from the first program code is
loaded on the virtual machine instance or the data store; and prior
to completion of the download of the first program code, cause the
second program code to be executed on the virtual machine instance
using the one or more execution parameters included in the first
request to execute the first program code despite the identifier
identifying the first program code and not the second program
code.
18. The non-transitory physical computer storage of claim 17,
wherein the instructions, when executed by the one or more
processors, further cause the one or more processors to, based at
least on a size of the first program code, cause the second program
code to be executed on the virtual machine instance.
19. The non-transitory physical computer storage of claim 17,
wherein the instructions, when executed by the one or more
processors, further cause the one or more processors to, based on
an indication that the second program code can be executed instead
of the first program code, cause the second program code to be
executed on the virtual machine instance.
20. The non-transitory physical computer storage of claim 17,
wherein the instructions, when executed by the one or more
processors, further cause the one or more processors to: receive a
second request to execute the first program code, the second
request including the identifier identifying the first program code
and one or more execution parameters for executing the first
program code; and determine that the virtual machine instance does
not have any container usable for executing the second program code
using the one or more execution parameters included in the second
request; and subsequent to completion of the download of the first
program code, cause the first program code to be executed using the
one or more execution parameters included in the second request
inside a container created on the virtual machine instance.
Description
BACKGROUND
Generally described, computing devices utilize a communication
network, or a series of communication networks, to exchange data.
Companies and organizations operate computer networks that
interconnect a number of computing devices to support operations or
provide services to third parties. The computing systems can be
located in a single geographic location or located in multiple,
distinct geographic locations (e.g., interconnected via private or
public communication networks). Specifically, data centers or data
processing centers, herein generally referred to as a "data
center," may include a number of interconnected computing systems
to provide computing resources to users of the data center. The
data centers may be private data centers operated on behalf of an
organization or public data centers operated on behalf, or for the
benefit of, the general public.
To facilitate increased utilization of data center resources,
virtualization technologies may allow a single physical computing
device to host one or more instances of virtual machines that
appear and operate as independent computing devices to users of a
data center. With virtualization, the single physical computing
device can create, maintain, delete, or otherwise manage virtual
machines in a dynamic manner. In turn, users can request computer
resources from a data center, including single computing devices or
a configuration of networked computing devices, and be provided
with varying numbers of virtual machine resources.
In some scenarios, virtual machine instances may be configured
according to a number of virtual machine instance types to provide
specific functionality. For example, various computing devices may
be associated with different combinations of operating systems or
operating system configurations, virtualized hardware resources and
software applications to enable a computing device to provide
different desired functionalities, or to provide similar
functionalities more efficiently. These virtual machine instance
type configurations are often contained within a device image,
which includes static data containing the software (e.g., the OS
and applications together with their configuration and data files,
etc.) that the virtual machine will run once started. The device
image is typically stored on the disk used to create or initialize
the instance. Thus, a computing device may process the device image
in order to implement the desired software configuration.
BRIEF DESCRIPTION OF THE DRAWINGS
The foregoing aspects and many of the attendant advantages of this
disclosure will become more readily appreciated as the same become
better understood by reference to the following detailed
description, when taken in conjunction with the accompanying
drawings, wherein:
FIG. 1 is a block diagram depicting an illustrative environment for
providing low latency compute capacity, according to an example
aspect;
FIGS. 2-5 are block diagrams illustrating an example versioning
scheme, according to an example aspect;
FIG. 6 depicts a general architecture of a computing device
providing a versioning and deployment manager for managing code
deployment on a virtual compute system, according to an example
aspect;
FIG. 7 is a flow diagram illustrating an example code deployment
routine implemented by a deployment manager, according to an
example aspect;
DETAILED DESCRIPTION
Companies and organizations no longer need to acquire and manage
their own data centers in order to perform computing operations
(e.g., execute code, including threads, programs, software,
routines, subroutines, processes, etc.). With the advent of cloud
computing, storage space and compute power traditionally provided
by hardware computing devices can now be obtained and configured in
minutes over the Internet. Thus, developers can quickly purchase a
desired amount of computing resources without having to worry about
acquiring physical machines. Such computing resources are typically
purchased in the form of virtual computing resources, or virtual
machine instances. These instances of virtual machines, which are
hosted on physical computing devices with their own operating
systems and other software components, can be utilized in the same
manner as physical computers.
However, even when virtual computing resources are purchased,
developers still have to decide how many and what type of virtual
machine instances to purchase, and how long to keep them. For
example, the costs of using the virtual machine instances may vary
depending on the type and the number of hours they are rented. In
addition, the minimum time a virtual machine may be rented is
typically on the order of hours. Further, developers have to
specify the hardware and software resources (e.g., type of
operating systems and language runtimes, etc.) to install on the
virtual machines. Other concerns that they might have include
over-utilization (e.g., acquiring too little computing resources
and suffering performance issues), under-utilization (e.g.,
acquiring more computing resources than necessary to run the codes,
and thus overpaying), prediction of change in traffic (e.g., so
that they know when to scale up or down), and instance and language
runtime startup delay, which can take 3-10 minutes, or longer, even
though users may desire computing capacity on the order of seconds
or even milliseconds. Thus, an improved method of allowing users to
take advantage of the virtual machine instances provided by service
providers is desired.
According to aspects of the present disclosure, by dynamically
deploying code in response to receiving code execution requests,
the delay (sometimes referred to as latency) associated with
executing the code (e.g., instance and language runtime startup
time) can be significantly reduced.
Generally described, aspects of the present disclosure relate to
the acquisition of user code and the deployment of the user code
onto the virtual compute system (e.g., internal storage, virtual
machine instances, and/or containers therein). Specifically,
systems and methods are disclosed which facilitate management of
user code within the virtual compute system. The virtual compute
system maintains a pool of virtual machine instances that have one
or more software components (e.g., operating systems, language
runtimes, libraries, etc.) loaded thereon. The virtual machine
instances in the pool can be designated to service user requests to
execute program codes. The program codes can be executed in
isolated containers that are created on the virtual machine
instances. Since the virtual machine instances in the pool have
already been booted and loaded with particular operating systems
and language runtimes by the time the requests are received, the
delay associated with finding compute capacity that can handle the
requests (e.g., by executing the user code in one or more
containers created on the virtual machine instances) is
significantly reduced.
In another aspect, a virtual compute system may determine that the
user code associated with an incoming request is an updated version
of the code that has already been loaded onto the virtual compute
system. Based on the nature of the incoming request and the state
of the virtual compute system, the virtual compute system may
determine where the code should be placed and which version of the
code should be used to service which request.
Specific embodiments and example applications of the present
disclosure will now be described with reference to the drawings.
These embodiments and example applications are intended to
illustrate, and not limit, the present disclosure.
With reference to FIG. 1, a block diagram illustrating an
embodiment of a virtual environment 100 will be described. The
example shown in FIG. 1 includes a virtual environment 100 in which
users (e.g., developers, etc.) of user computing devices 102 may
run various program codes using the virtual computing resources
provided by a virtual compute system 110.
By way of illustration, various example user computing devices 102
are shown in communication with the virtual compute system 110,
including a desktop computer, laptop, and a mobile phone. In
general, the user computing devices 102 can be any computing device
such as a desktop, laptop, mobile phone (or smartphone), tablet,
kiosk, wireless device, and other electronic devices. In addition,
the user computing devices 102 may include web services running on
the same or different data centers, where, for example, different
web services may programmatically communicate with each other to
perform one or more techniques described herein. Further, the user
computing devices 102 may include Internet of Things (IoT) devices
such as Internet appliances and connected devices. The virtual
compute system 110 may provide the user computing devices 102 with
one or more user interfaces, command-line interfaces (CLI),
application programming interfaces (API), and/or other programmatic
interfaces for generating and uploading user codes, invoking the
user codes (e.g., submitting a request to execute the user codes on
the virtual compute system 110), scheduling event-based jobs or
timed jobs, tracking the user codes, and/or viewing other logging
or monitoring information related to their requests and/or user
codes. Although one or more embodiments may be described herein as
using a user interface, it should be appreciated that such
embodiments may, additionally or alternatively, use any CLIs, APIs,
or other programmatic interfaces.
The user computing devices 102 access the virtual compute system
110 over a network 104. The network 104 may be any wired network,
wireless network, or combination thereof. In addition, the network
104 may be a personal area network, local area network, wide area
network, over-the-air broadcast network (e.g., for radio or
television), cable network, satellite network, cellular telephone
network, or combination thereof. For example, the network 104 may
be a publicly accessible network of linked networks, possibly
operated by various distinct parties, such as the Internet. In some
embodiments, the network 104 may be a private or semi-private
network, such as a corporate or university intranet. The network
104 may include one or more wireless networks, such as a Global
System for Mobile Communications (GSM) network, a Code Division
Multiple Access (CDMA) network, a Long Term Evolution (LTE)
network, or any other type of wireless network. The network 104 can
use protocols and components for communicating via the Internet or
any of the other aforementioned types of networks. For example, the
protocols used by the network 104 may include Hypertext Transfer
Protocol (HTTP), HTTP Secure (HTTPS), Message Queue Telemetry
Transport (MQTT), Constrained Application Protocol (CoAP), and the
like. Protocols and components for communicating via the Internet
or any of the other aforementioned types of communication networks
are well known to those skilled in the art and, thus, are not
described in more detail herein.
The virtual compute system 110 is depicted in FIG. 1 as operating
in a distributed computing environment including several computer
systems that are interconnected using one or more computer
networks. The virtual compute system 110 could also operate within
a computing environment having a fewer or greater number of devices
than are illustrated in FIG. 1. Thus, the depiction of the virtual
compute system 110 in FIG. 1 should be taken as illustrative and
not limiting to the present disclosure. For example, the virtual
compute system 110 or various constituents thereof could implement
various Web services components, hosted or "cloud" computing
environments, and/or peer-to-peer network configurations to
implement at least a portion of the processes described herein.
Further, the virtual compute system 110 may be implemented in
hardware and/or software and may, for instance, include one or more
physical or virtual servers implemented on physical computer
hardware configured to execute computer executable instructions for
performing various features that will be described herein. The one
or more servers may be geographically dispersed or geographically
co-located, for instance, in one or more data centers.
In the environment illustrated FIG. 1, the virtual environment 100
includes a virtual compute system 110, which includes a frontend
120, a warming pool manager 130, a worker manager 140, a versioning
and deployment manager 150, and an internal data store 160. In the
depicted example, virtual machine instances ("instances") 152, 154
are shown in a warming pool 130A managed by the warming pool
manager 130, and instances 156, 158 are shown in an active pool
140A managed by the worker manager 140. The illustration of the
various components within the virtual compute system 110 is logical
in nature and one or more of the components can be implemented by a
single computing device or multiple computing devices. For example,
the instances 152, 154, 156, 158 can be implemented on one or more
physical computing devices in different various geographic regions.
Similarly, each of the frontend 120, the warming pool manager 130,
the worker manager 140, the versioning and deployment manager 150,
and the internal data store 160 can be implemented across multiple
physical computing devices. Alternatively, one or more of the
frontend 120, the warming pool manager 130, the worker manager 140,
the versioning and deployment manager 150, and the internal data
store 160 can be implemented on a single physical computing device.
In some embodiments, the virtual compute system 110 may comprise
multiple frontends, multiple warming pool managers, multiple worker
managers, multiple deployment managers, and/or multiple internal
data stores. Although four virtual machine instances are shown in
the example of FIG. 1, the embodiments described herein are not
limited as such, and one skilled in the art will appreciate that
the virtual compute system 110 may comprise any number of virtual
machine instances implemented using any number of physical
computing devices. Similarly, although a single warming pool and a
single active pool are shown in the example of FIG. 1, the
embodiments described herein are not limited as such, and one
skilled in the art will appreciate that the virtual compute system
110 may comprise any number of warming pools and active pools.
In the example of FIG. 1, the virtual compute system 110 is
illustrated as being connected to the network 104. In some
embodiments, any of the components within the virtual compute
system 110 can communicate with other components (e.g., the user
computing devices 102 and auxiliary services 106, which may include
monitoring/logging/billing services 107, storage service 108, an
instance provisioning service 109, and/or other services that may
communicate with the virtual compute system 110) of the virtual
environment 100 via the network 104. In other embodiments, not all
components of the virtual compute system 110 are capable of
communicating with other components of the virtual environment 100.
In one example, only the frontend 120 may be connected to the
network 104, and other components of the virtual compute system 110
may communicate with other components of the virtual environment
100 via the frontend 120.
Users may use the virtual compute system 110 to execute user code
thereon. For example, a user may wish to run a piece of code in
connection with a web or mobile application that the user has
developed. One way of running the code would be to acquire virtual
machine instances from service providers who provide infrastructure
as a service, configure the virtual machine instances to suit the
user's needs, and use the configured virtual machine instances to
run the code. Alternatively, the user may send a code execution
request to the virtual compute system 110. The virtual compute
system 110 can handle the acquisition and configuration of compute
capacity (e.g., containers, instances, etc., which are described in
greater detail below) based on the code execution request, and
execute the code using the compute capacity. The virtual compute
system 110 may automatically scale up and down based on the volume,
thereby relieving the user from the burden of having to worry about
over-utilization (e.g., acquiring too little computing resources
and suffering performance issues) or under-utilization (e.g.,
acquiring more computing resources than necessary to run the codes,
and thus overpaying).
The frontend 120 processes all the requests to execute user code on
the virtual compute system 110. In some embodiments, the frontend
120 serves as a front door to all the other services provided by
the virtual compute system 110. The frontend 120 processes the
requests and makes sure that the requests are properly authorized.
For example, the frontend 120 may determine whether the user
associated with the request is authorized to access the user code
specified in the request.
The user code as used herein may refer to any program code (e.g., a
program, routine, subroutine, thread, etc.) written in a specific
program language. In the present disclosure, the terms "code,"
"user code," and "program code," may be used interchangeably. Such
user code may be executed to achieve a specific task, for example,
in connection with a particular web application or mobile
application developed by the user. For example, the user codes may
be written in JavaScript (node.js), Java, Python, and/or Ruby. The
request may include the user code (or the location thereof) and one
or more arguments to be used for executing the user code. For
example, the user may provide the user code along with the request
to execute the user code. In another example, the request may
identify a previously uploaded program code (e.g., using the API
for uploading the code) by its name or its unique ID. In yet
another example, the code may be included in the request as well as
uploaded in a separate location (e.g., the storage service 108 or a
storage system internal to the virtual compute system 110) prior to
the request is received by the virtual compute system 110. The
virtual compute system 110 may vary its code execution strategy
based on where the code is available at the time the request is
processed.
The frontend 120 may receive the request to execute such user codes
in response to Hypertext Transfer Protocol Secure (HTTPS) requests
from a user. Also, any information (e.g., headers and parameters)
included in the HTTPS request may also be processed and utilized
when executing the user code. As discussed above, any other
protocols, including, for example, HTTP, MQTT, and CoAP, may be
used to transfer the message containing the code execution request
to the frontend 120. The frontend 120 may also receive the request
to execute such user codes when an event is detected, such as an
event that the user has registered to trigger automatic request
generation. For example, the user may have registered the user code
with an auxiliary service 106 and specified that whenever a
particular event occurs (e.g., a new file is uploaded), the request
to execute the user code is sent to the frontend 120.
Alternatively, the user may have registered a timed job (e.g.,
execute the user code every 24 hours). In such an example, when the
scheduled time arrives for the timed job, the request to execute
the user code may be sent to the frontend 120. In yet another
example, the frontend 120 may have a queue of incoming code
execution requests, and when the user's batch job is removed from
the virtual compute system's work queue, the frontend 120 may
process the user request. In yet another example, the request may
originate from another component within the virtual compute system
110 or other servers or services not illustrated in FIG. 1.
A user request may specify one or more third-party libraries
(including native libraries) to be used along with the user code.
In one embodiment, the user request is a ZIP file containing the
user code and any libraries (and/or identifications of storage
locations thereof). In some embodiments, the user request includes
metadata that indicates the program code to be executed, the
language in which the program code is written, the user associated
with the request, and/or the computing resources (e.g., memory,
etc.) to be reserved for executing the program code. For example,
the program code may be provided with the request, previously
uploaded by the user, provided by the virtual compute system 110
(e.g., standard routines), and/or provided by third parties. In
some embodiments, such resource-level constraints (e.g., how much
memory is to be allocated for executing a particular user code) are
specified for the particular user code, and may not vary over each
execution of the user code. In such cases, the virtual compute
system 110 may have access to such resource-level constraints
before each individual request is received, and the individual
requests may not specify such resource-level constraints. In some
embodiments, the user request may specify other constraints such as
permission data that indicates what kind of permissions that the
request has to execute the user code. Such permission data may be
used by the virtual compute system 110 to access private resources
(e.g., on a private network).
In some embodiments, the user request may specify the behavior that
should be adopted for handling the user request. In such
embodiments, the user request may include an indicator for enabling
one or more execution modes in which the user code associated with
the user request is to be executed. For example, the request may
include a flag or a header for indicating whether the user code
should be executed in a debug mode in which the debugging and/or
logging output that may be generated in connection with the
execution of the user code is provided back to the user (e.g., via
a console user interface). In such an example, the virtual compute
system 110 may inspect the request and look for the flag or the
header, and if it is present, the virtual compute system 110 may
modify the behavior (e.g., logging facilities) of the container in
which the user code is executed, and cause the output data to be
provided back to the user. In some embodiments, the behavior/mode
indicators are added to the request by the user interface provided
to the user by the virtual compute system 110. Other features such
as source code profiling, remote debugging, etc. may also be
enabled or disabled based on the indication provided in the
request.
In some embodiments, the virtual compute system 110 may include
multiple frontends 120. In such embodiments, a load balancer may be
provided to distribute the incoming requests to the multiple
frontends 120, for example, in a round-robin fashion. In some
embodiments, the manner in which the load balancer distributes
incoming requests to the multiple frontends 120 may be based on the
state of the warming pool 130A and/or the active pool 140A. For
example, if the capacity in the warming pool 130A is deemed to be
sufficient, the requests may be distributed to the multiple
frontends 120 based on the individual capacities of the frontends
120 (e.g., based on one or more load balancing restrictions). On
the other hand, if the capacity in the warming pool 130A is less
than a threshold amount, one or more of such load balancing
restrictions may be removed such that the requests may be
distributed to the multiple frontends 120 in a manner that reduces
or minimizes the number of virtual machine instances taken from the
warming pool 130A. For example, even if, according to a load
balancing restriction, a request is to be routed to Frontend A, if
Frontend A needs to take an instance out of the warming pool 130A
to service the request but Frontend B can use one of the instances
in its active pool to service the same request, the request may be
routed to Frontend B.
The warming pool manager 130 ensures that virtual machine instances
are ready to be used by the worker manager 140 when the virtual
compute system 110 receives a request to execute user code on the
virtual compute system 110. In the example illustrated in FIG. 1,
the warming pool manager 130 manages the warming pool 130A, which
is a group (sometimes referred to as a pool) of pre-initialized and
pre-configured virtual machine instances that may be used to
service incoming user code execution requests. In some embodiments,
the warming pool manager 130 causes virtual machine instances to be
booted up on one or more physical computing machines within the
virtual compute system 110 and added to the warming pool 130A. In
other embodiments, the warming pool manager 130 communicates with
an auxiliary service (e.g., the instance provisioning service 109
of FIG. 1) to create and add new instances to the warming pool
130A. In some embodiments, the warming pool manager 130 may utilize
both physical computing devices within the virtual compute system
110 and one or more virtual machine instance services to acquire
and maintain compute capacity that can be used to service code
execution requests received by the frontend 120. In some
embodiments, the virtual compute system 110 may comprise one or
more logical knobs or switches for controlling (e.g., increasing or
decreasing) the available capacity in the warming pool 130A. For
example, a system administrator may use such a knob or switch to
increase the capacity available (e.g., the number of pre-booted
instances) in the warming pool 130A during peak hours. In some
embodiments, virtual machine instances in the warming pool 130A can
be configured based on a predetermined set of configurations
independent from a specific user request to execute a user's code.
The predetermined set of configurations can correspond to various
types of virtual machine instances to execute user codes. The
warming pool manager 130 can optimize types and numbers of virtual
machine instances in the warming pool 130A based on one or more
metrics related to current or previous user code executions.
As shown in FIG. 1, instances may have operating systems (OS)
and/or language runtimes loaded thereon. For example, the warming
pool 130A managed by the warming pool manager 130 comprises
instances 152, 154. The instance 152 includes an OS 152A and a
runtime 152B. The instance 154 includes an OS 154A. In some
embodiments, the instances in the warming pool 130A may also
include containers (which may further contain copies of operating
systems, runtimes, user codes, etc.), which are described in
greater detail below. Although the instance 152 is shown in FIG. 1
to include a single runtime, in other embodiments, the instances
depicted in FIG. 1 may include two or more runtimes, each of which
may be used for running a different user code. In some embodiments,
the warming pool manager 130 may maintain a list of instances in
the warming pool 130A. The list of instances may further specify
the configuration (e.g., OS, runtime, container, etc.) of the
instances.
In some embodiments, the virtual machine instances in the warming
pool 130A may be used to serve any user's request. In one
embodiment, all the virtual machine instances in the warming pool
130A are configured in the same or substantially similar manner. In
another embodiment, the virtual machine instances in the warming
pool 130A may be configured differently to suit the needs of
different users. For example, the virtual machine instances may
have different operating systems, different language runtimes,
and/or different libraries loaded thereon. In yet another
embodiment, the virtual machine instances in the warming pool 130A
may be configured in the same or substantially similar manner
(e.g., with the same OS, language runtimes, and/or libraries), but
some of those instances may have different container
configurations. For example, two instances may have runtimes for
both Python and Ruby, but one instance may have a container
configured to run Python code, and the other instance may have a
container configured to run Ruby code. In some embodiments,
multiple warming pools 130A, each having identically-configured
virtual machine instances, are provided.
The warming pool manager 130 may pre-configure the virtual machine
instances in the warming pool 130A, such that each virtual machine
instance is configured to satisfy at least one of the operating
conditions that may be requested or specified by the user request
to execute program code on the virtual compute system 110. In one
embodiment, the operating conditions may include program languages
in which the potential user codes may be written. For example, such
languages may include Java, JavaScript, Python, Ruby, and the like.
In some embodiments, the set of languages that the user codes may
be written in may be limited to a predetermined set (e.g., set of 4
languages, although in some embodiments sets of more or less than
four languages are provided) in order to facilitate
pre-initialization of the virtual machine instances that can
satisfy requests to execute user codes. For example, when the user
is configuring a request via a user interface provided by the
virtual compute system 110, the user interface may prompt the user
to specify one of the predetermined operating conditions for
executing the user code. In another example, the service-level
agreement (SLA) for utilizing the services provided by the virtual
compute system 110 may specify a set of conditions (e.g.,
programming languages, computing resources, etc.) that user
requests should satisfy, and the virtual compute system 110 may
assume that the requests satisfy the set of conditions in handling
the requests. In another example, operating conditions specified in
the request may include: the amount of compute power to be used for
processing the request; the type of the request (e.g., HTTP vs. a
triggered event); the timeout for the request (e.g., threshold time
after which the request may be terminated); security policies
(e.g., may control which instances in the warming pool 130A are
usable by which user); and etc.
The worker manager 140 manages the instances used for servicing
incoming code execution requests. In the example illustrated in
FIG. 1, the worker manager 140 manages the active pool 140A, which
is a group (sometimes referred to as a pool) of virtual machine
instances that are currently assigned to one or more users.
Although the virtual machine instances are described here as being
assigned to a particular user, in some embodiments, the instances
may be assigned to a group of users, such that the instance is tied
to the group of users and any member of the group can utilize
resources on the instance. For example, the users in the same group
may belong to the same security group (e.g., based on their
security credentials) such that executing one member's code in a
container on a particular instance after another member's code has
been executed in another container on the same instance does not
pose security risks. Similarly, the worker manager 140 may assign
the instances and the containers according to one or more policies
that dictate which requests can be executed in which containers and
which instances can be assigned to which users. An example policy
may specify that instances are assigned to collections of users who
share the same account (e.g., account for accessing the services
provided by the virtual compute system 110). In some embodiments,
the requests associated with the same user group may share the same
containers (e.g., if the user codes associated therewith are
identical). In some embodiments, a request does not differentiate
between the different users of the group and simply indicates the
group to which the users associated with the requests belong.
In the example illustrated in FIG. 1, user codes are executed in
isolated compute systems referred to as containers. Containers are
logical units created within a virtual machine instance using the
resources available on that instance. For example, the worker
manager 140 may, based on information specified in the request to
execute user code, create a new container or locate an existing
container in one of the instances in the active pool 140A and
assigns the container to the request to handle the execution of the
user code associated with the request. In one embodiment, such
containers are implemented as Linux containers. The virtual machine
instances in the active pool 140A may have one or more containers
created thereon and have one or more program codes associated with
the user loaded thereon (e.g., either in one of the containers or
in a local cache of the instance). Each container may have
credential information made available therein, so that user codes
executing on the container have access to whatever the
corresponding credential information allows them to access.
As shown in FIG. 1, instances may have operating systems (OS),
language runtimes, and containers. The containers may have
individual copies of the OS and the language runtimes and user
codes loaded thereon. In the example of FIG. 1, the active pool
140A managed by the worker manager 140 includes the instances 156,
158. The instance 156 has containers 156A, 156B. The container 156A
has OS 156A-1, runtime 156A-2, and code 156A-3 loaded therein. In
the depicted example, the container 156A has its own OS, runtime,
and code loaded therein. In one embodiment, the OS 156A-1 (e.g.,
the kernel thereof), runtime 156A-2, and/or code 156A-3 are shared
among the containers 156A, 156B (and any other containers not
illustrated in FIG. 1). In another embodiment, the OS 156A-1 (e.g.,
any code running outside the kernel), runtime 156A-2, and/or code
156A-3 are independent copies that are created for the container
156A and are not shared with other containers on the instance 156.
In yet another embodiment, some portions of the OS 156A-1, runtime
156A-2, and/or code 156A-3 are shared among the containers on the
instance 156, and other portions thereof are independent copies
that are specific to the container 156A. The instance 158 includes
containers 158A, 158B and a code cache 159C for storing code
executed in any of the containers on the instance 158.
In the example of FIG. 1, the sizes of the containers depicted in
FIG. 1 may be proportional to the actual size of the containers.
For example, the container 156A may occupy more space than the
container 156B on the instance 156. Similarly, the containers 158A,
158B may be equally sized. The dotted boxes labeled "C" shown in
the instance 158 indicate the space remaining on the instances that
may be used to create new containers. In some embodiments, the
sizes of the containers may be 64 MB or any multiples thereof. In
other embodiments, the sizes of the containers may be any arbitrary
size smaller than or equal to the size of the instances in which
the containers are created. In some embodiments, the sizes of the
containers may be any arbitrary size smaller than, equal to, or
larger than the size of the instances in which the containers are
created. By how much the sizes of the containers can exceed the
size of the instance may be determined based on how likely that
those containers might be utilized beyond the capacity provided by
the instance.
Although the components inside the containers 156B, 158A are not
illustrated in the example of FIG. 1, each of these containers may
have various operating systems, language runtimes, libraries,
and/or user code. In some embodiments, instances may have user
codes loaded thereon (e.g., in an instance-level cache such as the
code cache 159C), and containers within those instances may also
have user codes loaded therein (e.g., container 156A). In some
embodiments, the worker manager 140 may maintain a list of
instances in the active pool 140A. The list of instances may
further specify the configuration (e.g., OS, runtime, container,
etc.) of the instances. In some embodiments, the worker manager 140
may have access to a list of instances in the warming pool 130A
(e.g., including the number and type of instances). In other
embodiments, the worker manager 140 requests compute capacity from
the warming pool manager 130 without having knowledge of the
virtual machine instances in the warming pool 130A.
After a request has been successfully processed by the frontend
120, the worker manager 140 finds capacity to service the request
to execute user code on the virtual compute system 110. For
example, if there exists a particular virtual machine instance in
the active pool 140A that has a container with the same user code
loaded therein (e.g., code 156A-3 shown in the container 156A), the
worker manager 140 may assign the container to the request and
cause the user code to be executed in the container. Alternatively,
if the user code is available in the local cache of one of the
virtual machine instances (e.g., stored in the code cache 159C of
the instance 158 but do not belong to any individual containers),
the worker manager 140 may create a new container on such an
instance, assign the container to the request, and cause the user
code to be loaded and executed in the container.
If the worker manager 140 determines that the user code associated
with the request is not found on any of the instances (e.g., either
in a container or the local cache of an instance) in the active
pool 140A, the worker manager 140 may determine whether any of the
instances in the active pool 140A is currently assigned to the user
associated with the request and has compute capacity to handle the
current request. If there is such an instance, the worker manager
140 may create a new container on the instance and assign the
container to the request. Alternatively, the worker manager 140 may
further configure an existing container on the instance assigned to
the user, and assign the container to the request. For example, the
worker manager 140 may determine that the existing container may be
used to execute the user code if a particular library demanded by
the current user request is loaded thereon. In such a case, the
worker manager 140 may load the particular library and the user
code onto the container and use the container to execute the user
code.
If the active pool 140A does not contain any instances currently
assigned to the user, the worker manager 140 pulls a new virtual
machine instance from the warming pool 130A, assigns the instance
to the user associated with the request, creates a new container on
the instance, assigns the container to the request, and causes the
user code to be downloaded and executed on the container.
In some embodiments, the virtual compute system 110 is adapted to
begin execution of the user code shortly after it is received
(e.g., by the frontend 120). A time period can be determined as the
difference in time between initiating execution of the user code
(e.g., in a container on a virtual machine instance associated with
the user) and receiving a request to execute the user code (e.g.,
received by a frontend). The virtual compute system 110 is adapted
to begin execution of the user code within a time period that is
less than a predetermined duration. In one embodiment, the
predetermined duration is 500 ms. In another embodiment, the
predetermined duration is 300 ms. In another embodiment, the
predetermined duration is 100 ms. In another embodiment, the
predetermined duration is 50 ms. In another embodiment, the
predetermined duration is 10 ms. In another embodiment, the
predetermined duration may be any value chosen from the range of 10
ms to 500 ms. In some embodiments, the virtual compute system 110
is adapted to begin execution of the user code within a time period
that is less than a predetermined duration if one or more
conditions are satisfied. For example, the one or more conditions
may include any one of: (1) the user code is loaded on a container
in the active pool 140A at the time the request is received; (2)
the user code is stored in the code cache of an instance in the
active pool 140A at the time the request is received; (3) the
active pool 140A contains an instance assigned to the user
associated with the request at the time the request is received; or
(4) the warming pool 130A has capacity to handle the request at the
time the request is received.
The user code may be downloaded from an auxiliary service 106 such
as the storage service 108 of FIG. 1. Data 108A illustrated in FIG.
1 may comprise user codes uploaded by one or more users, metadata
associated with such user codes, or any other data utilized by the
virtual compute system 110 to perform one or more techniques
described herein. Although only the storage service 108 is
illustrated in the example of FIG. 1, the virtual environment 100
may include other levels of storage systems from which the user
code may be downloaded. For example, each instance may have one or
more storage systems either physically (e.g., a local storage
resident on the physical computing system on which the instance is
running) or logically (e.g., a network-attached storage system in
network communication with the instance and provided within or
outside of the virtual compute system 110) associated with the
instance on which the container is created. Alternatively, the code
may be downloaded from a web-based data store provided by the
storage service 108.
Once the worker manager 140 locates one of the virtual machine
instances in the warming pool 130A that can be used to serve the
user code execution request, the warming pool manager 130 or the
worker manger 140 takes the instance out of the warming pool 130A
and assigns it to the user associated with the request. The
assigned virtual machine instance is taken out of the warming pool
130A and placed in the active pool 140A. In some embodiments, once
the virtual machine instance has been assigned to a particular
user, the same virtual machine instance cannot be used to service
requests of any other user. This provides security benefits to
users by preventing possible co-mingling of user resources.
Alternatively, in some embodiments, multiple containers belonging
to different users (or assigned to requests associated with
different users) may co-exist on a single virtual machine instance.
Such an approach may improve utilization of the available compute
capacity. In some embodiments, the virtual compute system 110 may
maintain a separate cache in which user codes are stored to serve
as an intermediate level of caching system between the local cache
of the virtual machine instances and a web-based network storage
(e.g., accessible via the network 104).
After the user code has been executed, the worker manager 140 may
tear down the container used to execute the user code to free up
the resources it occupied to be used for other containers in the
instance. Alternatively, the worker manager 140 may keep the
container running to use it to service additional requests from the
same user. For example, if another request associated with the same
user code that has already been loaded in the container, the
request can be assigned to the same container, thereby eliminating
the delay associated with creating a new container and loading the
user code in the container. In some embodiments, the worker manager
140 may tear down the instance in which the container used to
execute the user code was created. Alternatively, the worker
manager 140 may keep the instance running to use it to service
additional requests from the same user. The determination of
whether to keep the container and/or the instance running after the
user code is done executing may be based on a threshold time, the
type of the user, average request volume of the user, and/or other
operating conditions. For example, after a threshold time has
passed (e.g., 5 minutes, 30 minutes, 1 hour, 24 hours, 30 days,
etc.) without any activity (e.g., running of the code), the
container and/or the virtual machine instance is shutdown (e.g.,
deleted, terminated, etc.), and resources allocated thereto are
released. In some embodiments, the threshold time passed before a
container is torn down is shorter than the threshold time passed
before an instance is torn down.
In some embodiments, the virtual compute system 110 may provide
data to one or more of the auxiliary services 106 as it services
incoming code execution requests. For example, the virtual compute
system 110 may communicate with the monitoring/logging/billing
services 107. The monitoring/logging/billing services 107 may
include: a monitoring service for managing monitoring information
received from the virtual compute system 110, such as statuses of
containers and instances on the virtual compute system 110; a
logging service for managing logging information received from the
virtual compute system 110, such as activities performed by
containers and instances on the virtual compute system 110; and a
billing service for generating billing information associated with
executing user code on the virtual compute system 110 (e.g., based
on the monitoring information and/or the logging information
managed by the monitoring service and the logging service). In
addition to the system-level activities that may be performed by
the monitoring/logging/billing services 107 (e.g., on behalf of the
virtual compute system 110) as described above, the
monitoring/logging/billing services 107 may provide
application-level services on behalf of the user code executed on
the virtual compute system 110. For example, the
monitoring/logging/billing services 107 may monitor and/or log
various inputs, outputs, or other data and parameters on behalf of
the user code being executed on the virtual compute system 110.
Although shown as a single block, the monitoring, logging, and
billing services 107 may be provided as separate services.
In some embodiments, the worker manager 140 may perform health
checks on the instances and containers managed by the worker
manager 140 (e.g., those in the active pool 140A). For example, the
health checks performed by the worker manager 140 may include
determining whether the instances and the containers managed by the
worker manager 140 have any issues of (1) misconfigured networking
and/or startup configuration, (2) exhausted memory, (3) corrupted
file system, (4) incompatible kernel, and/or any other problems
that may impair the performance of the instances and the
containers. In one embodiment, the worker manager 140 performs the
health checks periodically (e.g., every 5 minutes, every 30
minutes, every hour, every 24 hours, etc.). In some embodiments,
the frequency of the health checks may be adjusted automatically
based on the result of the health checks. In other embodiments, the
frequency of the health checks may be adjusted based on user
requests. In some embodiments, the worker manager 140 may perform
similar health checks on the instances and/or containers in the
warming pool 130A. The instances and/or the containers in the
warming pool 130A may be managed either together with those
instances and containers in the active pool 140A or separately. In
some embodiments, in the case where the health of the instances
and/or the containers in the warming pool 130A is managed
separately from the active pool 140A, the warming pool manager 130,
instead of the worker manager 140, may perform the health checks
described above on the instances and/or the containers in the
warming pool 130A.
The versioning and deployment manager 150 manages the deployment of
user code on the virtual compute system 110. For example, the
versioning and deployment manager 150 may communicate with the
frontend 120, the warming pool manager 130, the worker manager 140,
and/or the internal data store 160 to manage the deployment of user
code onto any internal data store, instance-level code cache,
and/or containers on the virtual compute system 110. Although the
versioning and deployment manager 150 is illustrated as a distinct
component within the virtual compute system 110, part or all of the
functionalities of the versioning and deployment manager 150 may be
performed by the frontend 120, the warming pool manager 130, the
worker manager 140, and/or the internal data store 160. For
example, the versioning and deployment manager 150 may be
implemented entirely within one of the other components of the
virtual compute system 110 or in a distributed manner across the
other components of the virtual compute system 110. In the example
of FIG. 1, the versioning and deployment manager 150 includes code
deployment data 150A. The code deployment data 150A may include
data regarding the history of incoming requests, versions of the
user code executed on the virtual compute system 110, and any other
metric that may be used by the versioning and deployment manager
150 to adjust and/or optimize the deployment of the user code
associated with the incoming code execution requests. The code
deployment data 150A may also include any management policies
specified by the users or determined by the versioning and
deployment manager 150 for deploying their code (e.g., versioning
preferences, etc.) on the virtual compute system 110.
Throughout the lifecycle of a user code, various updates may be
made to the code. In some embodiments, the versioning and
deployment manager 150 maintains a list of all the user codes
executing on the virtual compute system 110, and when the
versioning and deployment manager 150 determines that one or more
of the users codes have been updated, the versioning and deployment
manager 150 causes the updated user codes to be used (instead of
the older versions thereof) in connection with subsequent code
execution requests received by the virtual compute system 110. For
example, when a user updates a particular user code using one API
and makes requests associated with the user code using another API,
the virtual compute system 110 may programmatically determine when
the requests associated with the user code should be processed with
the new version (e.g., based on the size of the new version,
availability of the older version in the active pool 130A, etc.).
In some embodiments, the request may include an indication that the
user code associated with the request has been updated. For
example, the user may specify that the code has been updated. In
another example, the user may specify the version of the code that
he or she wishes to use, and the versioning and deployment manager
150 may determine, for each request, whether the version specified
by the user is different from the one or more versions of the code
that might be running on the virtual compute system 110. In yet
another example, the request may include an identifier that is
unique to the code (e.g., date of creation, date of modification,
hash value, etc.). In other embodiments, the versioning and
deployment manager 150 may automatically determine, based on the
user code received along with the request, whether there has been
any updates to the user code. For example, the versioning and
deployment manager 150 may calculate a hash value or a checksum of
the code and determine whether the code is different from the one
or more versions of the code that might be running on the virtual
compute system 110.
In some embodiments, the versioning and deployment manager 150 may
cause one or more of the requests that are received after the user
code has been updated to be serviced using the older version of the
code. For example, when a new version of the user code is detected,
the versioning and deployment manager 150 may allow any containers
that are in the middle of executing the older version of the user
code to finish before loading the new version onto those
containers. In some embodiments, the versioning and deployment
manager 150 allows the older version of the code to be used while
the new version is being downloaded onto an internal data store, a
code cache of a particular instance, and/or a container. By using
the older version of the code while the new version is being
downloaded, any latency increase due to the change may be avoided
by overlaying the procurement of the new version (e.g., latest
corrected/request version) with the execution of the requests. In
some embodiments, the versioning and deployment manager 150 may
immediately start downloading the new version onto those containers
having the older version loaded thereon (or onto new containers
that are created on the instances on which those containers having
the older version loaded thereon are created), In other
embodiments, the versioning and deployment manager 150 may download
the new version onto those containers having the older version
loaded thereon (or onto new containers that are created on the
instances on which those containers having the older version loaded
thereon are created) after those containers become idle (e.g., not
currently executing any user code). In some embodiments, the
versioning and deployment manager 150 may determine how long it
might take to download the new version associated with a particular
request, and decide to service the particular request using the
older version if the download time exceeds a threshold value. In
some embodiments, the versioning and deployment manager 150 may
determine how many requests associated with the code are being
received, and decide to service one or more of the requests using
the older version if one or more containers already have the older
version loaded, and if not enough containers have the newer version
loaded thereon to serve all of the requests.
In some embodiments, the versioning and deployment manager 150
determines, based on a user request, how quickly the older versions
of the code should be removed from the virtual compute system 110.
For example, the user associated with the code execution request
may indicate that the update is a minor one and that the user would
prefer the latency to be as low as possible. In such an example,
the versioning and deployment manager 150 may keep running the
older versions of the code and gradually phase in the newer version
(e.g., when the instances running the older version have all been
discarded or otherwise not available or have enough capacity to
handle all the incoming requests associated with the code). In
another example, the user may indicate that the older versions have
a security bug that is exposing customers' credit card information
and that all previous versions of the code should be killed
immediately. In such an example, the versioning and deployment
manager 150 may stop and/or terminate any containers running the
older versions of the code and begin using the newer version
immediately.
In some embodiments, the versioning and deployment manager 150,
based on the history of the volume of requests received by the
virtual compute system 110, may preemptively load a program code
that is sufficiently frequently executed on the virtual compute
system 110 onto one or more containers in the active pool 140A. In
some embodiments, the versioning and deployment manager 150 causes
certain codes to remain in the container and/or the instance if the
code is anticipated to be executed in a cyclical manner. For
example, if the versioning and deployment manager 150 determines
that the virtual compute system 110 a particular code receives 90%
of its requests between the hours of 7 PM and 8 PM, the versioning
and deployment manager 150 may cause the particular code to be
remain in the containers even after hours of inactivity. In some
embodiments, the versioning and deployment manager 150 may
preemptively load the new version onto one or more containers in
the active pool 140A or the warming pool 130A, when the versioning
and deployment manager 150 detects the new version, even before any
request associated with the new version is received.
In the example of FIG. 1, the versioning and deployment manager 150
maintains the internal data store 160 that is used to store data
accessed by one or more instances. For example, the versioning and
deployment manager 150 may store user code onto the internal data
store 160 so that the user code can be shared among multiple
instances. In some embodiments, the data stored on the internal
data store 160 in connection with such multiple instances (e.g.,
user codes executed in containers created on such instances)
remains on the internal data store 160 for use by other instances
even after the particular instance is shut down. In one example,
downloading a code onto a container from the internal data store
160 is more than 10 times faster than downloading the same code
onto a container from a data store external to the virtual compute
system 110 (e.g., storage service 108). In some embodiments, the
internal data store 160 is divided into isolated containers (which
may provide additional security among such containers), and access
to each container is restricted to one or more instances associated
therewith.
The versioning and deployment manager 150 may include a code
deployment unit for analyzing incoming code execution requests
received by the virtual compute system 110 and determining where
and how user code should be acquired and deployed. An example
configuration of the versioning and deployment manager 150 is
described in greater detail below with reference to FIG. 6.
With reference to FIGS. 2-5, an example versioning scheme for
handling code execution requests after user code has been updated
will be described. In the example of FIG. 2, the storage service
106 has code (1) 108B ("code (1)") loaded thereon, and the instance
158 has a code cache 158C with the code (1) loaded thereon and four
containers that are busy executing the code (1) 108B. The dotted
boxes labeled "C" shown in the instance 158 indicate the space
remaining on the instances that may be used to create new
containers.
In FIG. 3, the code (1) previously stored on the storage service
108 has been updated to code (2) 108C ("code (2)"). FIG. 3 also
shows that one of the containers has become idle, and the other
three containers are still busy executing code (1) (e.g., in
connection with existing requests associated with the code (1) or
new requests associated with the code (2)). For example, the three
containers are still executing the now-out-of-date code (1) even
after the code has been updated to code (2), for example, to reduce
the latency associated with servicing the request. The versioning
and deployment manager 150 may have initiated the download of the
code (2) at this point.
In FIG. 4, the code (2) has finished downloading onto the code
cache 159C. The code (2) has also been loaded onto the previously
idle container and two new containers. The first three containers
are still executing the code (1) (e.g., servicing new requests
associated with the code (2) using the code (1) loaded
thereon).
FIG. 5 illustrates the configuration after all the containers have
switched to the code (2) and are running the code (2) in connection
with incoming code execution requests. In FIG. 5, the code (1) has
also been removed from the code cache 159C, for example, by the
versioning and deployment manager 150 after it has determined
(e.g., based on the time elapsed since the code (1) was updated, or
based on a user indication to eventually phase out the code (1))
that the code (1) is no longer needed. In some embodiments, after
the code (2) has been fully phased in, assuming the level of
incoming code execution requests does not change, the same number
of containers (e.g., four in the example of FIGS. 2-5) may be able
to handle the incoming code execution requests associated with a
particular code, regardless of which version of the particular code
is used.
Thus, by continuing to service incoming code execution requests
using an older version of the code even after the code has been
updated, existing containers having the older version of the code
loaded thereon can be utilized to reduce the latency associated
with processing the code execution requests.
FIG. 6 depicts a general architecture of a computing system
(referenced as versioning and deployment manager 150) that manages
the deployment of user code in the virtual compute system 110. The
general architecture of the versioning and deployment manager 150
depicted in FIG. 6 includes an arrangement of computer hardware and
software modules that may be used to implement aspects of the
present disclosure. The versioning and deployment manager 150 may
include many more (or fewer) elements than those shown in FIG. 6.
It is not necessary, however, that all of these generally
conventional elements be shown in order to provide an enabling
disclosure. As illustrated, the versioning and deployment manager
150 includes a processing unit 190, a network interface 192, a
computer readable medium drive 194, an input/output device
interface 196, all of which may communicate with one another by way
of a communication bus. The network interface 192 may provide
connectivity to one or more networks or computing systems. The
processing unit 190 may thus receive information and instructions
from other computing systems or services via the network 104. The
processing unit 190 may also communicate to and from memory 180 and
further provide output information for an optional display (not
shown) via the input/output device interface 196. The input/output
device interface 196 may also accept input from an optional input
device (not shown).
The memory 180 may contain computer program instructions (grouped
as modules in some embodiments) that the processing unit 190
executes in order to implement one or more aspects of the present
disclosure. The memory 180 generally includes RAM, ROM and/or other
persistent, auxiliary or non-transitory computer-readable media.
The memory 180 may store an operating system 184 that provides
computer program instructions for use by the processing unit 190 in
the general administration and operation of the versioning and
deployment manager 150. The memory 180 may further include computer
program instructions and other information for implementing aspects
of the present disclosure. For example, in one embodiment, the
memory 180 includes a user interface unit 182 that generates user
interfaces (and/or instructions therefor) for display upon a
computing device, e.g., via a navigation and/or browsing interface
such as a browser or application installed on the computing device.
In addition, the memory 180 may include and/or communicate with one
or more data repositories (not shown), for example, to access user
program codes and/or libraries.
In addition to and/or in combination with the user interface unit
182, the memory 180 may include a code deployment unit 186 that may
be executed by the processing unit 190. In one embodiment, the user
interface unit 182, and code deployment unit 186 individually or
collectively implement various aspects of the present disclosure,
e.g., analyzing incoming code execution requests received by the
virtual compute system 110, determining where and how user code
should be acquired and deployed, etc. as described further
below.
The code deployment unit 186 analyzes incoming code execution
requests received by the virtual compute system 110. For example,
the code deployment unit 186 may determine whether the user code
associated with an incoming request is a newer version of a code
that is loaded on one or more of the containers of the virtual
compute system 110. Based on the nature of the incoming request and
the state of the virtual compute system 110, the code deployment
unit 186 determines where the code should be placed and which code
should be used to service which request.
While the code deployment unit 186 is shown in FIG. 6 as part of
the versioning and deployment manager 150, in other embodiments,
all or a portion of the code deployment unit 186 may be implemented
by other components of the virtual compute system 110 and/or
another computing device. For example, in certain embodiments of
the present disclosure, another computing device in communication
with the virtual compute system 110 may include several modules or
components that operate similarly to the modules and components
illustrated as part of the versioning and deployment manager
150.
Turning now to FIG. 7, a routine 700 implemented by one or more
components of the virtual compute system 110 (e.g., the versioning
and deployment manager 150) will be described. Although routine 700
is described with regard to implementation by the versioning and
deployment manager 150, one skilled in the relevant art will
appreciate that alternative components may implement routine 700 or
that one or more of the blocks may be implemented by a different
component or in a distributed manner.
At block 702 of the illustrative routine 700, the versioning and
deployment manager 150 receives a code execution request associated
a user code. For example, the versioning and deployment manager 150
may receive the request from the frontend 120 after the frontend
has performed any initial processing on the request. As discussed
above, the request may specify the code to be executed on the
virtual compute system 110, and any operating conditions such as
the amount of compute power to be used for processing the request,
the type of the request (e.g., HTTP vs. a triggered event), the
timeout for the request (e.g., threshold time after which the
request may be terminated), security policies (e.g., may control
which instances in the warming pool 130A are usable by which user),
etc.
At block 704, the versioning and deployment manager 150 detects
that the code associated with the request is an updated version of
a code that has already been loaded onto the virtual compute system
110. For example, one or more containers may have the older version
of the code associated with the request loaded thereon.
At block 706, the versioning and deployment manager 150 initiates a
download of the updated version of the code onto the virtual
compute system 110. For example, the versioning and deployment
manager 150 cause the updated version of the code to be downloaded
onto an internal data store of the virtual compute system 110
(e.g., internal data store 160 of FIG. 1), a code cache on one of
the instances (e.g., code cache 158C of FIG. 1), or one or more
containers created on the virtual compute system 110.
At block 708, the versioning and deployment manager 150 causes the
code execution request associated with the updated version of the
code to be processed with an older version of the code that was
previously loaded on one of the containers before the code
execution request was received at block 702.
While the routine 700 of FIG. 7 has been described above with
reference to blocks 702-708, the embodiments described herein are
not limited as such, and one or more blocks may be added, omitted,
modified, or switched without departing from the spirit of the
present disclosure. For example, the routine 700 may further
include block 710, where the versioning and deployment manager 150
causes a subsequent code execution request associated with the
updated version of the code to be processed with the updated
version of the code loaded onto one of the containers after the
download initiated at block 706 has completed.
It will be appreciated by those skilled in the art and others that
all of the functions described in this disclosure may be embodied
in software executed by one or more physical processors of the
disclosed components and mobile communication devices. The software
may be persistently stored in any type of non-volatile storage.
Conditional language, such as, among others, "can," "could,"
"might," or "may," unless specifically stated otherwise, or
otherwise understood within the context as used, is generally
intended to convey that certain embodiments include, while other
embodiments do not include, certain features, elements and/or
steps. Thus, such conditional language is not generally intended to
imply that features, elements and/or steps are in any way required
for one or more embodiments or that one or more embodiments
necessarily include logic for deciding, with or without user input
or prompting, whether these features, elements and/or steps are
included or are to be performed in any particular embodiment.
Any process descriptions, elements, or blocks in the flow diagrams
described herein and/or depicted in the attached figures should be
understood as potentially representing modules, segments, or
portions of code which include one or more executable instructions
for implementing specific logical functions or steps in the
process. Alternate implementations are included within the scope of
the embodiments described herein in which elements or functions may
be deleted, executed out of order from that shown or discussed,
including substantially concurrently or in reverse order, depending
on the functionality involved, as would be understood by those
skilled in the art. It will further be appreciated that the data
and/or components described above may be stored on a
computer-readable medium and loaded into memory of the computing
device using a drive mechanism associated with a computer readable
storage medium storing the computer executable components such as a
CD-ROM, DVD-ROM, or network interface. Further, the component
and/or data can be included in a single device or distributed in
any manner. Accordingly, general purpose computing devices may be
configured to implement the processes, algorithms, and methodology
of the present disclosure with the processing and/or execution of
the various data and/or components described above.
It should be emphasized that many variations and modifications may
be made to the above-described embodiments, the elements of which
are to be understood as being among other acceptable examples. All
such modifications and variations are intended to be included
herein within the scope of this disclosure and protected by the
following claims.
* * * * *
References